Achieve Efficient and Privacy-Preserving Compound Substring Query over Cloud

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users’ query functionality into consideration. Searchable symmetric encryption (SSE) is a popular solution that can support efficient attribute queries over encrypted datasets in the cloud. In particular, some SSE schemes focus on the substring query, which deals with the situation that the user only remembers the substring of the queried attribute. However, all of them just consider substring queries on a single attribute, which cannot be used to achieve compound substring queries on multiple attributes. This paper aims to address this issue by proposing an efficient and privacy-preserving SSE scheme supporting compound substring queries. In specific, we first employ the position heap technique to design a novel tree-based index to support substring queries on a single attribute and employ pseudorandom function (PRF) and fully homomorphic encryption (FHE) techniques to protect its privacy. Then, based on the homomorphism of FHE, we design a filter algorithm to calculate the intersection of search results for different attributes, which can be used to support compound substring queries on multiple attributes. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate the efficiency of our proposed scheme.

Application of seismic multiattribute machine learning to determine coal strata thickness

The coal mining industry is developing automated and intelligent coal mining processes. Accurate determination of the geological conditions of working faces is an important prerequisite for automated mining. The use of machine learning to extract comprehensive attributes from seismic data and the application of that data to determine the coal strata thickness has become an important area of research in recent years. Conventional coal strata thickness interpretation methods do not meet the application requirements of mines. Determining the coal strata thickness with machine learning solves this problem to a large extent, especially for issues of exploration accuracy. In this study, we use seismic exploration data from the Xingdong coal mine, with the 1225 working face as the research object, and we apply seismic multiattribute machine learning to determine the coal strata thickness. First, through optimal selection, we perform seismic multiattribute extraction and optimal multiparameter selection by selecting the seismic attributes with good responses to the coal strata thickness and extracting training samples. Second, we optimise the model through a trial-and-error method and use machine learning for training. Finally, we illustrate the advantages of this method using actual data. We compare the results of the proposed model with results based on a single attribute, The results show that application of seismic multiattribute machine learning to determine coal strata thickness meets the requirements of geological inspection and has a good application performance and practical significance in complex areas.

Evaluation of resources in the study of industrial and culinary properties for the varietal characterization of rice (Oryza sativa L.)

Different physicochemical tests have been used over time in the Oryza sativa species for the study of industrial and culinary properties. In this study, nine attributes were compared: amylose content, total whiteness, thousand grain weight, grain length and width, gelatinization time, apparent water absorption, expansion ratio, and gelatinization temperature. However, there is no single attribute that allows defining the concept of “culinary quality” in rice, since it depends on its behavior after cooking. The two varieties of long fine polished rice used for this study were: IRGA 424 (Rio Grandense Rice Institute) and Tranquilo FL INTA (National Institute of Agricultural Technology) and a mixture of both. In this work, the characterization of industrial and culinary properties of the rice varieties mentioned above was achieved by evaluating five attributes using simple and low-cost physical-chemical techniques in two of the rice varieties most in demand by producers in the province of Corrientes. This finding would allow the study of rice varieties to be approached by optimizing: equipment, reagents and time in future trials.

A predictive model for the ichnological suitability of the Jezero crater, Mars: searching for fossilized traces of life-substrate interactions in the 2020 Rover Mission Landing Site

Ichnofossils, the fossilized products of life-substrate interactions, are among the most abundant biosignatures on Earth and therefore they may provide scientific evidence of potential life that may have existed on Mars. Ichnofossils offer unique advantages in the search for extraterrestrial life, including the fact that they are resilient to processes that obliterate other evidence for past life, such as body fossils, as well as chemical and isotopic biosignatures. The goal of this paper is evaluating the suitability of the Mars 2020 Landing Site for ichnofossils. To this goal, we apply palaeontological predictive modelling, a technique used to forecast the location of fossil sites in uninvestigated areas on Earth. Accordingly, a geographic information system (GIS) of the landing site is developed. Each layer of the GIS maps the suitability for one or more ichnofossil types (bioturbation, bioerosion, biostratification structures) based on an assessment of a single attribute (suitability factor) of the Martian environment. Suitability criteria have been selected among the environmental attributes that control ichnofossil abundance and preservation in 18 reference sites on Earth. The goal of this research is delivered through three predictive maps showing which areas of the Mars 2020 Landing Site are more likely to preserve potential ichnofossils. On the basis of these maps, an ichnological strategy for the Perseverance rover is identified, indicating (1) 10 sites on Mars with high suitability for bioturbation, bioerosion and biostratification ichnofossils, (2) the ichnofossil types, if any, that are more likely to be present at each site, (3) the most efficient observation strategy for detecting eventual ichnofossils. The predictive maps and the ichnological strategy can be easily integrated in the existing plans for the exploration of the Jezero crater, realizing benefits in life-search efficiency and cost-reduction.

A New Method to Balance Measurement Accuracy and Attribute Coverage in Cognitive Diagnostic Computerized Adaptive Testing

As one of the important research areas of cognitive diagnosis assessment, cognitive diagnostic computerized adaptive testing (CD-CAT) has received much attention in recent years. Measurement accuracy is the major theme in CD-CAT, and both the item selection method and the attribute coverage have a crucial effect on measurement accuracy. A new attribute coverage index, the ratio of test length to the number of attributes (RTA), is introduced in the current study. RTA is appropriate when the item pool comprises many items that measure multiple attributes where it can both produce acceptable measurement accuracy and balance the attribute coverage. With simulations, the new index is compared to the original item selection method (ORI) and the attribute balance index (ABI), which have been proposed in previous studies. The results show that (1) the RTA method produces comparable measurement accuracy to the ORI method under most item selection methods; (2) the RTA method produces higher measurement accuracy than the ABI method for most item selection methods, with the exception of the mutual information item selection method; (3) the RTA method prefers items that measure multiple attributes, compared to the ORI and ABI methods, while the ABI prefers items that measure a single attribute; and (4) the RTA method performs better than the ORI method with respect to attribute coverage, while it performs worse than the ABI with long tests.

Quota-based debiasing can decrease representation of the most under-represented groups

Many important decisions in societies such as school admissions, hiring or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example, via the introduction of quotas that ensure a proportional representation of groups with respect to a certain, often binary attribute. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes, we show that quota-based debiasing based on a single attribute can worsen the representation of the most under-represented intersectional groups and decrease the overall fairness of selection. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.

Synergy effect of nutritional labelling on purchase intention of sugar-sweetened beverages

PurposeThe purpose of this study is to investigate how combined nutrition labelling influences consumer attitudes, subjective norms, perceived behavioural control and purchase intention for sugar-sweetened beverages (SSBs).Design/methodology/approachThis study adopted a mixed method research, quasi-experimental design with 406 valid Taiwanese samples to evaluate the possible effects of combined nutrition labelling on SSB purchase intention; two focus group interviews with four nutritional experts and 12 students were conducted to explain how and why consumers perceived different types of combined nutritional labels.FindingsCombined labels including sugar type/gram with the guideline daily amounts and traffic light display were perceived by consumers as high-quality and reliable, which improved consumer attitude and SSB purchase intention. Consumers perceived the traffic light display and warning claim as a sugar over-consumption message, which reduced SSB purchase intention through subject norms.Practical implicationsGovernments should be aware that concrete nutritional information (NIP) leads to the worst SSB consumption. Moreover, the authors suggest that policymakers emphasise the effectiveness of warning claims on SSB products with “sufficient” sugar information to trigger consumers' concern, remind SSB manufacturers of their moral obligation to consumers.Originality/valueThis study identified that the combined effects of nutritional attributes and parts of meanings might be enhanced, eliminated or even separated from their original meaning. Although the label messages were delivered simultaneously, the consumer's psychological perceptions proved to be more complicated than a single attribute and sequentially affected consumer attitudes, subject norms and SSB purchase intention.

Mitigating Demographic Bias in Facial Datasets with Style-Based Multi-attribute Transfer

Deep learning has catalysed progress in tasks such as face recognition and analysis, leading to a quick integration of technological solutions in multiple layers of our society. While such systems have proven to be accurate by standard evaluation metrics and benchmarks, a surge of work has recently exposed the demographic bias that such algorithms exhibit–highlighting that accuracy does not entail fairness. Clearly, deploying biased systems under real-world settings can have grave consequences for affected populations. Indeed, learning methods are prone to inheriting, or even amplifying the bias present in a training set, manifested by uneven representation across demographic groups. In facial datasets, this particularly relates to attributes such as skin tone, gender, and age. In this work, we address the problem of mitigating bias in facial datasets by data augmentation. We propose a multi-attribute framework that can successfully transfer complex, multi-scale facial patterns even if these belong to underrepresented groups in the training set. This is achieved by relaxing the rigid dependence on a single attribute label, and further introducing a tensor-based mixing structure that captures multiplicative interactions between attributes in a multilinear fashion. We evaluate our method with an extensive set of qualitative and quantitative experiments on several datasets, with rigorous comparisons to state-of-the-art methods. We find that the proposed framework can successfully mitigate dataset bias, as evinced by extensive evaluations on established diversity metrics, while significantly improving fairness metrics such as equality of opportunity.